[ ⌖ ] Pathwise
Decision Instrument · v1.0
Built for USAII Global AI Hackathon 2026 — Undergrad Track, Challenge 3

Plot the decision. /* don't guess it */
See where each path actually leads.

Grad school or a job. A startup or a steady salary. Pathwise models the tradeoffs across time instead of handing you a pros-and-cons list — and it always shows its uncertainty instead of faking confidence.

01Choose paths
02Your context
03Weigh priorities
04Read the chart

Set up your comparison

Pick two real paths you're actually choosing between. Adjust the numbers to match your situation — the model reacts to them, it doesn't just illustrate a canned example.

Optional. Without a key, Pathwise uses its built-in template narrator — the tradeoff numbers themselves are identical either way, since those come from the rules engine, not the LLM.

The chart

Each line is a path. Each gate is a horizon. Faded nodes mean the model is less sure — that fade is doing real work, not decoration.

What a pros/cons list would have missed

These don't come from either path alone — they emerge from comparing your specific combination of paths and context.

What this actually means for you

Run a comparison above to generate this.

Why this needs AI, and where it stops

Two separate layers, on purpose — one mechanical, one linguistic. Neither pretends to be the other.
Layer 1 — Rules

Tradeoff scoring

Deterministic and auditable. Same inputs always produce the same scores. This is where the actual "reasoning structure" lives — four lenses × three time horizons, with confidence bands instead of fake precision. No hallucination risk because nothing here is generated.

Layer 2 — Optional LLM

Plain-language explanation

The rules engine can't read "my parents really want me to play it safe" in your free-text box and adjust its tone. An LLM can. It only ever explains the numbers already computed — it's instructed never to override them, and the app works fully without it.

Human-in-the-loop

The decision itself

Pathwise will never output a field called "recommendation." It computes a weighted lean, always shows the runner-up's strongest dimension, and the actual choice — the one with real consequences — stays entirely with you.